1. Field of Invention
The present invention relates to a method and a device of monitoring and fault detection in industrial processes. More specifically, the present invention relates to a method of applying multivariate techniques in the sequential transfer of quality parameters by means of score values and monitoring the process with early fault detection.
2. Description of Related Art
In today's world of outsourcing, many of the steps in manufacturing process are actually made by other companies than by the company responsible for the final product. Examples of products assembled in such a way include cars, computers, and telephone exchanges. The same approach applies to a product manufactured in several steps without assembly, e.g., a pharmaceutical tablet, a roll of printing paper, or a wafer in a semiconductor process. Sequential manufacturing makes for a strong need of tracking quality data through the whole manufacturing tree, assuring that all components and sub-components, as well as their combinations, have adequate quality, faults are early (high up) discovered in the tree, etc. When multiple data are measured in process steps and component quality testing, only multivariate tools can adequately use the information to evaluate the quality in the tree of manufacturing steps.
The products of today must meet increasing quality demands. The product quality is measured by many parameters and depends on many process variables in each step of the process chain. During each manufacturing step, process data are measured at certain intervals to control and monitor the process, and after each step intermediate quality data are measured on the sub-components and components, and then final quality data are measured on the final product. However, even if many quality measurements are used, with reliance on traditional Statistical Process Control (SPC) the small tolerated variation of each component leads to major difficulties, and rejection of a portion of well working product.
Several techniques are used for the purpose of monitoring a process. Parameters to check include quality, yield, energy, product rejection, etc. A conventional approach for monitoring a process is to consider one variable at a time (univariate SPC). This approach is not adequate for obtaining the best quality, economy, etc. of a product in a manufacturing process, since actually several variables are involved.
The most commonly employed type of SPC uses single variable control charts. When a given product or process is outside of a specification it is indicated. The limitation with SPC is that only few variables, generally at the most around 5, can be used for monitoring the process.
The quality of intermediate and end products is in most cases described by values of a set of variables, the product specification. The specifications are often used in a univariate mode, i.e. they are checked individually for conformation within the specification value range. This gives rise to both false negative and false positive classification, since the quality variables very rarely are independent in practice, but are treated as if they were, i.e. univariately.
It is possible to use traditional SPC to establish when a process is out of specification when only a few variables are involved. However, when the number of process variables and quality increase or when they interact, problems arise. Very often it is difficult to determine the source of the problem, particularly when the number of process variables increases. Product quality is typically a multivariate property and must be treated as such in order to monitor a process in that respect.
In order to optimize and control a process with several variables, projection techniques such as Principal Components Analysis (PCA) and Projection to Latent Structure (PLS) have been applied. These techniques are well described (Mac Gregor et. al.) and further development has been made to address the process control need of today. S. Wold et al., “Hierarchical multi-block PLS and PC models, for easier interpretation, and as an alternative to variable selection,” J. CHEMOMETRICS 10 (1996), pages 463-482, describes a method where variables are divided into conceptually meaningful blocks before applying hierarchical multi-block PLS or PC models. This allows an interpretation focused on pertinent blocks and their dominant variables. Such blocking can be used in process modeling and modeling.
Attempts based on SPC and projection techniques have been made to control a process. For example, WO 99/19780 describes a method and device for controlling an essentially continuous process comprising at least two sub-processes, which minimizes the rejection of the produced product. The method is based upon combining multivariate models with a processed variable value. A variable value for a subsequent second sub process is predicted based on the combination of the multivariate model and the processed variable value. However, the method only utilizes multivariate data analysis with respect to controlling the process and not for checking or monitoring it. Furthermore, the method is applied only for a specific application and cannot be used in general applications.
C. WIKSTROM et al., “Multivariate process and quality monitoring applied to an electrolysis process,” Part 1. Process supervision with multivariate control charts Chemometrics and intelligent laboratory system 42 (1998) pages 221-231), describes Multi-variate Statistical Process Control (MSPC) applied to an electrolysis process and the benefit with multivariate analysis over traditional univariate analysis also is discussed Moreover, the article shows how the result from a multivariate principal component analysis method can be displayed graphically in multivariate statistical control charts. By using this informationally efficient MSPC approach, rather than any inefficient SPC technique, the potential of achieving major improvements in the under-tanding and monitoring of the process is shown. The improvements are, however, not sufficient to be able to control the quality problems in complex processes unless specific experimentation has been made to make the multivariate model invertable and thus also capable to determine how the process should be modified to minimize deviation from the specification profile. In MSPC as well as SPC “controlling” should be synonymous with “checking” or “monitoring”.
Another drawback with the application of prior art to sequential manufacturing is the need to carry relevant information from different process steps in a process chain, which cannot be easily achieved by known techniques. Therefore, a need exists to describe product quality in a sequential monitoring process by a multivariate model of the relevant quality variables rather than the individual variables themselves.
A problem with prior art (univariate SPC) is that the quality variables are not independent, but their interdependencies get lost if they are analyzed or monitored individually. The risk for false product approval increases and when this occurs and is fed back in the supply chain the specification intervals are usually narrowed in order to secure product. However, this rarely eliminates the problem of false product approval and also give rise to substantial false rejects.